search（8）- elastic4s-search-query模式

  上篇提过query模式除对记录的筛选之外还对符合条件的记录进行了评分，即与条件的相似匹配程度。我们把评分放在后面的博文中讨论，这篇我们只介绍query查询。

查询可以分为绝对值查询和全文查询：绝对值查询是指非text类型字段的查询，全文查询一般指对于text字段的查询。如果需要对text字段进行绝对值查询的话可以用fields在text字段下定义一个keyword字段。text类型字段在建索引时会经分词器处理分解成许多单词，然后在查询时查询目标也会经历分词处理后才逐个单词进行匹配。所以要注意录入的查询条件不一定是最终的查询内容，因为会首先进行分词处理。

我们先看几个绝对值查询例子：

POST /bank/_search
{
  "query" : {
    "term" : {
       "state.keyword": "IL"
    }
  }
}
POST /bank/_search
{
  "query" : {
    "terms" : {
       "state.keyword": ["IL","WA"]
    }
  }
}
POST /bank/_search
{
  "query" : {
    "range" : {
       "age": {
         "gte" : 20,
         "lte" : 40
       }
    }
  }
}

POST /bank/_search
{
  "query" : {
    "prefix" : {
       "address.keyword": "880"
    }
  }
}
POST /bank/_search
{
  "query" : {
    "wildcard": {
       "address.keyword": "*Holmes*"
    }
  }
}
POST /bank/_search
{
  "query" : {
    "regexp": {
       "address.keyword": ".*Holmes.*"
    }
  }
}

elastic4s的表达形式如下：

 val qTerm = search("bank").query(termQuery("state.keyword","IL"))
  val qTerms = search("bank").query(termsQuery("state.keyword","IL","WA"))
  val qRange = search("bank").query(rangeQuery("age").gte(20).lte(40))
  val qPrefix = search("bank").query(prefixQuery("address.keyword","880"))
  val qWildcard = search("bank").query(wildcardQuery("address.keyword","*Holmes*"))
  val qRegex = search("bank").query(regexQuery("address.keyword",".*Holmes.*"))

全文查询最简单的例子就是match query 了：

POST /bank/_search
{
  "query" : {
    "match" : { "address" : "holmes"}
  }
}

 val qMatch = search("bank").query(matchQuery("address","holmes"))

以上是个单字查询示范。多字全文查询如下：

POST /bank/_search
{
  "query" : {
    "match" : { "address" : "holmes lane"}
  }
}

 val qMMatch = search("bank").query(matchQuery("address","holmes lane"))

问题出现了：查询结果不但有"880 Holmes Lane"还包括了"685 School Lane"，这是因为分词器把"holmes lane" 分解成了"holmes","lane"两个单字，而多字查询默认关系是or，只要包含holmes,lane任何一项都符合条件。我们可以用and关联：

POST /bank/_search
{
  "query" : {
    "match" : { 
      "address" : {
        "query": "holmes lane",
        "operator": "and"
      }
    }
  }
}

  val qMMatchAnd = search("bank").query(matchQuery("address","holmes lane").operator("and"))

现在结果只剩下"880 Holmes Lane"一条了。下面这个query与之有同效：

POST /bank/_search
{
  "query" : {
    "match" : { 
      "address" : {
        "query": "holmes lane",
        "minimum_should_match": "100%"
      }
    }
  }
}

  val qMMatchMin = search("bank").query(matchQuery("address","holmes lane").minimumShouldMatch("100%"))

以上例子都是简单类型的查询，即单语句查询。现实中我们普遍需要用and,or来结合多种条件形成复合式查询。最具代表性的也就是boolQuery了。boolQuery的格式如下：

GET /bank/_search
{
  "query": {
    "bool": {
      "must": [    // lastname=duke and gender.keyword = M
        { "match": { "lastname":   "duke" }},
        { "term": { "gender.keyword": "M" }}
      ],
      "must_not": [  // and firstname.keyword != Jackson and city.keyword != Jackson
        { "term": { "firstname.keyword":   "Jackson"}},
        { "term": { "city.keyword": "Brogan" }}
      ],
      "should": [  // or email.keyword = *.cn or age >= 80
        { "wildcard": { "email.keyword":   "*.cn" }},
        { "range": { "age": {"gte" : 80}}}
      ],    
      "filter": [  // filter state.keyword in (IL,WA,TA) and balance >= 100000
        { "terms":  { "state.keyword": ["IL","WA","TA"] }},
        { "range": { "balance": { "gte": 100000 }}}
      ]
    }
  }
}

在elastic4s里这样表示：

  val qBool = search("bank").query(
    boolQuery().must(
      matchQuery("lastname","duke"),
      termQuery("gender.keyword","M")
    ).not(
      termQuery("fistname.keyword","Jackson"),
      termQuery("city.keyword","Brogan")
    ).should(
      termsQuery("state.keyword",Seq("IL","WA","TA")),
      rangeQuery("balance").gte(100000)
    )
  )

上面例子里的must,must_no,should,filter各段落可以单独或联合形式任意出现在boolQuery里。在任何段落里还可以嵌入boolQuery, 如下：

GET /bank/_search
{
  "query": {
    "bool": {
      "must": [
        { "match": { "lastname":   "duke" }},
        { "term": { "gender.keyword": "M" }}
      ],
      "must_not": [
        { "term": { "firstname.keyword":   "Jackson"}},
        { "term": { "city.keyword": "Brogan" }}
      ],
      "should": [
        { "wildcard": { "email.keyword":   "*.cn" }},
        { "range": { "age": {"gte" : 80}}}
      ],    
      "filter": [
        { "terms":  { "state.keyword": ["IL","WA","TA"] }},
        { "range": { "balance": { "gte": 100000 }}},
        {
          "bool" : {
            "should" : [
               {"range" : {"balance" :{"gte" : 1000}}}
              ]
          }
        }
      ]
    }
  }
}